Convex Optimization
Generalized channel inversion methods for multiuser MIMO systems
IEEE Transactions on Communications
Degrees of freedom of the K user M × N MIMO interference channel
IEEE Transactions on Information Theory
Optimized signaling for MIMO interference systems with feedback
IEEE Transactions on Signal Processing
Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels
IEEE Transactions on Signal Processing
Sum capacity of the vector Gaussian broadcast channel and uplink-downlink duality
IEEE Transactions on Information Theory
Iterative water-filling for Gaussian vector multiple-access channels
IEEE Transactions on Information Theory
On achievable rate regions for the Gaussian interference channel
IEEE Transactions on Information Theory
Limited feedback unitary precoding for spatial multiplexing systems
IEEE Transactions on Information Theory
Degrees of Freedom for the MIMO Interference Channel
IEEE Transactions on Information Theory
Interference Alignment and Degrees of Freedom of the -User Interference Channel
IEEE Transactions on Information Theory
Gaussian Interference Channel Capacity to Within One Bit
IEEE Transactions on Information Theory
Queue proportional scheduling via geometric programming in fading broadcast channels
IEEE Journal on Selected Areas in Communications
Spatial multiplexing gain for two interfering MIMO broadcast channels based on linear transceiver
IEEE Transactions on Wireless Communications
Distributed beamforming techniques for weighted sum-rate maximization in MISO interference channels
IEEE Communications Letters
An iterative algorithm for multi-user inference channel based on subspace projection
International Journal of Wireless and Mobile Computing
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This paper studies linear precoding and decoding schemes for K-user interference channel systems. It was shown by Cadambe and Jafar that the interference alignment (IA) algorithm achieves a theoretical bound on degrees of freedom (DOF) for interference channel systems. Based on this, we first introduce a non-iterative solution for the precoding and decoding scheme. To this end, we determine the orthonormal basis vectors of each user's precoding matrix to achieve the maximum DOF, then we optimize precoding matrices in the IA method according to two different decoding schemes with respect to individual rate. Second, an iterative processing algorithm is proposed which maximizes the weighted sum rate. Deriving the gradient of the weighted sum rate and applying the gradient descent method, the proposed scheme identifies a local-optimal solution iteratively. Simulation results show that the proposed iterative algorithm outperforms other existing methods in terms of sum rate. Also, we exhibit that the proposed non-iterative method approaches a local optimal solution at high signal-to-noise ratio with reduced complexity.